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A task scheduling method for agent/activity-based models

Book Contribution - Book Chapter Conference Contribution

Estimation of spatio-temporal travel demand requires accurate activity schedules as an input along with a mechanism to adapt the schedules to changing travel options. Individuals are assumed to own a duty list of activities to be accomplished within the simulated period. A partial order based on chronological and functional constraints determines the set of feasible activity execution sequences (plans). Trip and activity timing is determined by schedule prediction and adaptation. Event times in a schedule are constrained by conditions involving time-of-day (absolute time) and by duration constraints (relative time). Both types of constraints are expressed using time deviation functions (TDF). Each start and end event in a schedule induces a set of non-linear equations expressing the absolute and relative constraints. Time values are determined by solving the set of non-linear equations using a relaxation method. A discrepancy evaluation function is used both as a criterion to decide convergence of the relaxation and to compare alternative schedules for a given plan.
Book: The 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018) / The 8th International Conference on Sustainable Energy Information Technology (SEIT-2018) / Affiliated Workshops
Series: Procedia Computer Science
Pages: 761 - 766
Publication year:2018
Keywords:agent-based simulation, schedule generation, schedule adaptation, travel demand
BOF-keylabel:yes
IOF-keylabel:yes
Accessibility:Open